AN ESTIMATING EQUATIONS APPROACH FOR THE LISCOMP MODEL

Maximum likelihood estimation is computatonally infeasible for latent variable models involving multivariate categorical responses, in parti cular for the LISCOMP model. A three-stage generalized least squares a pproach introduced by Muthen (1983, 1984) can experience problems of i nstability, bias, non-convergence, and non-positive definiteness of we ight matrices in situations of low prevalence, small sample size and l arge numbers of observed indicator variables. We propose a quadratic e stimating equations approach that only requires specification of the f irst two moments. By performing simultaneous estimation of parameters, this method does not encounter the problems mentioned above and exper iences gains in efficiency. Methods are compared through a numerical s tudy and an application to a study of life-events and neurotic illness .